A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting

Location fingerprinting using received signal strengths (RSSs) from wireless network infrastructure is one of the most popular and promising technologies for localization in an indoor environment, where there is no line-of-sight signal from the global positioning system (GPS) available [1]: For example, a vector of pairs of a service set identifier (SSID) and an RSS for a Wi-Fi access point (AP) measured at a location can be its location fingerprint. A location of a user/device then can be estimated by finding the closest match between its RSS measurement and the fingerprints of known locations in a database [2]. Note that the location fingerprinting technique does not require the installation of any new infrastructure or the modification of existing devices, but it is just based on the existing wireless infrastructure, which is its major advantage over alternative techniques. When the indoor localization is to cover a large building complex -- e.g., a big shopping mall or a university campus -- where there are lots of multistory buildings under the same management, the scalability of fingerprinting techniques becomes an important issue. The current state-of-the-art Wi-Fi fingerprinting techniques assume a hierarchical approach to the indoor localization, where the building, floor, and position (e.g., a label or coordinates) of a location are estimated in a hierarchical and sequential way using a different algorithm tailored for each task.